Knowledge Management Framework
The Semantic
Web is an extension of the current web in which information is given a
well-defined meaning, enabling better cooperation between computers and
people. The Semantic Web is the abstract representation of data on the
World Wide Web, based on the RDF standards. The Semantic Web provides
a powerful platform for developing the knowledge management systems. One
main problem is in representing knowledge in the machine-understandable
form, so that relevant knowledge can be easily found by machine agents.
The Knowledge Management Framework enables efficient management of knowledge
sources on the Semantic Web, using presented conditional descriptions
for a more effective searching for knowledge. The Knowledge Management
Framework features a knowledge management approach based on Resource Description
Framework (RDF) compatible format. The Knowledge Management Framework
is based on existing Semantic Web tools such as OntOMat, OntOMat-SOEP,
OntOMat-REVERSE, OntOMat-CRAWL, and Ontobroker, which should be slightly
extended in order to operate with more expressible data format.
The main
process in a knowledge management system is the finding of knowledge sources
which are uses to solve some knowledge related problems. Based on knowledge
formalisation, the knowledge sources can be divided into two categories
which include formal expert rules and documents. For more efficient searching
of the knowledge contained in the documents, the content of the documents
is indexed using some ontology-based statements. Here the statements have
a conditional form. The precondition-action statement enables the usage
of the same logical mechanisms to manage both categories of knowledge
sources. Sometimes, a search for relevant knowledge results in some expert
rules and/or some documents.
Knowledge
can be collected from various sources and in different formats, then stored
in the common representation formalism, processed in order to compute
interdependencies between knowledge items or to resolve conflicts, shared/searched
and finally used for problem solving. The Knowledge Management Framework
encompasses the following processes such as knowledge capturing, knowledge
representation, knowledge processing, knowledge sharing, and using of
knowledge. All these processes are related somehow to the domain ontology.
Since ontology is a domain model, it contains a set of domain axioms which
are used for deriving new information.
Four types
of knowledge sources can be treated in the knowledge capturing phase which
includes expert knowledge, legacy rule-base systems, metadata repositories
and documents. Each of the knowledge source is associated with a Semantic
Web tool. You can capture the expert knowledge in the form of rules, using
a simple ontology editor plug-in OntOMat SOEP. The OntOMat SOEP is an
ontology editor that is extended with rule-editing capabilities. The OntOMat
SOEP provides structure as well as vocabulary, i.e. lexical layer of the
domain ontology, for the rule creation. Although these rules are related
to domain ontology, they are not treated as axioms in that ontology. The
ontological axioms should be always-true statements, which is not the
case for expert rules. The OntOMat SOEP saves the expert rules directly
in the RDFRule format.
Legacy rule-base
systems are very valuable sources of sharable knowledge, which can be
consulted in solving some problems, either for free or for some price.
The focus is not on collaborative problem solving via querying the federation
of rule bases, but in the creating high-specific expert bases, by importing
relevant rule chains from those rule bases. The OntOMat-REVERSE tool which
translates the content of a relational database into an ontology represented
in the RDF, for the support of this translation into RDFRule.
Metadata
will be the primary knowledge source for sharing knowledge in the future.
To make the sharing more efficient some mechanisms for knowledge packaging
and knowledge trading/pricing are needed. The OntOMat-CRAWL tool has capabilities
to collect web documents that fit the given knowledge model, so that the
adaptation to rule-crawling is straightforward. Knowledge are informally
represented in the documents, whereas the content of a document can be
formally stated by ontology-based indexes. This process is called semantic
annotation which is supported by the OntOMat annotation framework
Knowledge repository is a relational database organised in a way that
enables efficient storing and access to RDF metadata. This repository
can be seen as a RDF repository.
The knowledge
processing component enables efficient manipulation with the stored knowledge,
especially graph-based processing for the knowledge represented in the
form of rules. Knowledge sharing is realised by searching for rules that
satisfy the query conditions. Rules are related to domain ontology, which
contains domain axioms used for deriving new assertions. Therefore the
searching is realised as an inferencing process. The Ontobroker tool performs
the process of inferencing using RDF inputs. The system treats facts and
queries as rules without the rule body and the rule head, respectively.
This facility enables using the SOEP editor as a query interface.
The main
advantage of using the Knowledge Management Framework, is that a conditional
statement is used for the semantic annotation of knowledge sources. As
the statements used in the annotation are put into the context of each
other, this consequently leads to efficient searching for knowledge. Annotating
knowledge resources using Precondition-Action statements enable semantic
hyperlinking of each two resources, which satisfies the condition that
the Precondition part of one annotation, subsumes the Action part of the
annotation of another resource. As a result, querying for a problem can
result in a composition of documents, which cover problem solving. This
is a very important process in knowledge management or e-learning search.
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